Case studies

Examples of how broker inboxes become structured working data.

These anonymized case studies show what changed before and after LaycanMatch on real broker-style workflows: email volume, extracted offers, match shortlists and source-email review.

Short answer

These case studies are proof points, not fixture guarantees.

LaycanMatch helps brokers reduce inbox scanning, extract more structured cargo and vessel records, and find relevant matches faster. The broker still reviews the source email and makes the commercial decision.

Case study 01

Small dry bulk desk

A two-broker desk receiving 800 to 1,200 broker emails per week wanted a faster way to shortlist cargoes for open supramax and handy positions.

Case study small dry bulk desk
Before / after workflow snapshot
01

Context

The desk worked from one main authorized mailbox plus forwarded threads. Cargo and vessel ideas were tracked partly in memory and partly in spreadsheets.

02

Problem

Useful cargoes were being missed because brokers had to re-scan folders and old circulars every time a vessel opened in the Black Sea or East Med.

03

Setup

LaycanMatch processed one authorized mailbox and selected folders from the last 30 days.

04

Result

  • 1,046 emails processed
  • 184 cargo offers extracted
  • 96 vessel offers extracted
  • 37 ranked possible matches found
  • Average first-pass review time reduced from roughly 95 minutes to 28 minutes per day
  • Broker kept source email access for every match

Before / after: before LaycanMatch the desk depended on inbox search and memory; after LaycanMatch the desk worked from a structured shortlist linked back to the original circular.

Case study 02

Multi-mailbox broker team

A small team with several broker identities needed to compare cargo and vessel flow across more than one mailbox without creating duplicate offer noise.

Case study multi-mailbox broker team
Mailbox consolidation without silent duplication
01

Context

The team received overlapping broker circulars across three active mailboxes and struggled to tell whether an offer was genuinely new or just forwarded again.

02

Problem

Duplicate messages made the internal picture noisy and made it harder to see which offers were worth calling on first.

03

Setup

LaycanMatch processed 21 days of history across three selected inbox and archive folder sets with dedupe enabled.

04

Result

  • 2,382 emails processed
  • 412 cargo offers extracted
  • 233 vessel offers extracted
  • 286 duplicate messages surfaced as duplicates instead of new working records
  • 61 ranked possible matches found for current saved positions
  • Broker triage time on repeated circulars cut by more than half

Before / after: before LaycanMatch the same offer reappeared in several inboxes with no clear handling; after LaycanMatch the team saw one cleaner working set plus visible duplicate decisions.

Case study 03

Laycan-sensitive cargo workflow

A cargo-focused workflow needed better laycan visibility because vessel ideas were being filtered manually from ambiguous broker wording.

Case study laycan sensitive cargo workflow
Laycan review with source-email evidence
01

Context

The desk monitored repeated grain and fertilizer opportunities where laycan text often arrived as mid-July, prompt, 1H Aug, late Jul or 5 days either side.

02

Problem

Manual comparison consumed time and brokers still had to reopen the original circular to confirm what the sender meant.

03

Setup

LaycanMatch processed 14 days of selected broker folders and the desk created saved cargo requirements for the most repeated routes.

04

Result

  • 684 emails processed
  • 119 cargo offers extracted
  • 74 vessel offers extracted
  • 18 low-confidence laycan cases flagged for review
  • 29 ranked possible matches found for active cargo requirements
  • Broker still checked ambiguous cases, but the shortlist arrived faster and with source text preserved

Before / after: before LaycanMatch the desk manually compared date phrases; after LaycanMatch ambiguous cases stayed visible, but clear overlaps were ranked first.

Disclaimer

Broker judgment remains required in every case.

These examples show workflow improvement, not guaranteed commercial outcomes. LaycanMatch structures broker emails, highlights possible matches and keeps the source email available for review, but the broker still decides what is worth pursuing.

What LaycanMatch does not do

It does not guarantee fixtures, negotiate freight or replace broker judgment. The product improves review speed and working visibility, not the commercial reality of every opportunity.

Who this is not for

If a desk expects guaranteed commercial results from automation alone, these case studies are the wrong expectation. They are workflow proof points, not promise pages.

Related links

Test it on your own workflow

Start with 1,000 free processed emails.

Use a controlled mailbox, selected folders and a narrow date range first, then compare the shortlist against how your desk works today.